Objective: To describe the prevalence and association of sleep quality and anxiety-depression co-morbid symptoms among nursing students, in order to provide a reference basis for promoting the development of nursing students mental health. Methods: Using a prospective study design, baseline survey was conducted in January 2019 among a random cluster sample of 1 716 individuals in three medical universities in Hefei, Anhui Province, and a follow-up survey was conducted in October 2019, with a valid number of 1 573 individuals after matching with the baseline survey. The Pittsburgh Sleep Quality Index (PSQI) was used to assess nursing students sleep quality, and the Depression Anxiety Stress Scale (DASS-21) to assess the anxiety-depression comorbid symptoms. Results: The detection rates of anxiety-depression co-morbidities among nursing students at baseline and follow-up survey were 16.9% and 18.2%, respectively, and the detection rates of poor sleep quality among nursing students at baseline and follow-up survey were 10.1% and 10.3%, respectively. The results of the binary Logistic regression model showed that baseline PSQI score were positively associated with the risk of anxiety-depression co-morbid symptoms among nursing students at baseline ( OR=1.49, 95%CI =1.40-1.59) and after nine months of follow-up ( OR=1.22, 95%CI =1.16-1.28). Furthermore, the influence of baseline sleep quality on the risk of anxiety-depression co-morbid symptoms were mainly concentrated in the five dimensions of sleep time, sleep efficiency, sleep disorders, hypnotic drugs and daytime dysfunction, and such effects of sleep time, sleep disorders and daytime dysfunction still existed in the follow-up investigation. Conclusion Poor sleep quality of nursing students can increase the risk of anxiety-depression co-morbidities. Improving sleep quality of nursing students has a positive effect on improving their mental health.

OBJECTIVE: To analyze transforming growth factor-β1( TGF-β1)-induced differentially expressed genes( DEGs) in human embryonic lung fibroblast( IMR-90) using microarray,and to screen the key genes and signaling pathways related to trans-differentiation of fibroblast.METHODS: The gene chip GSE17518,attained from TGF-β1 stimulated IMR-90 cells,was downloaded from the Gene Expression Omnibus database.The DEGs were screened by GENE-E software.Then,the DEGs were imported into the DAVID online database for Gene Ontology( GO) functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes( KEGG) pathway enrichment analysis.The proteinprotein interaction( PPI) network was constructed and the hub genes were screened using STRING database and Cytoscape software.RESULTS: A total of 394 DEGs related to TGF-β1 stimulation were identified,including 171 down-regulated genes and 223 up-regulated genes.The results of GO analysis showed that the DEGs were widely distributed in cytoplasm,cell membrane,extracellular matrix( ECM) and exosomes,regulating biological functions such as ECM organization,cell migration and adhesion,cell proliferation and apoptosis.The results of the KEGG pathway analysis indicated that most of DEGs were enriched in cell focal adhesion,ECM-receptor interaction and phosphoinositide 3 kinase-Protein kinase B( PI3K-Akt) signaling pathways.The PPI network screened 10 core genes,included nucleolar protein 2( NOP2),succinate dehydrogenase B,glutamyl-prolyl-tRNA synthetase( EPRS),FtsJ homolog 3( FTSJ3),prefoldin subunit 4,Ras-related C3 botulinum toxin substrate 2,signal recognition particle receptor subunit beta,succinate-Co A ligase GDPforming beta subunit,pumilio RNA binding family member 3( KIAA0020),and general vesicular transport factor p115.NOP2,EPRS,FTSJ3,KIAA0020 were mainly distributed in M1 module.The NOP2 is the core gene with the highest number of nodes in M1 module.CONCLUSION: A total of 10 core differential genes and 7 signaling pathways related to TGF-β1 stimulation were screened.Among them,focal adhesion,ECM-receptor interaction,PI3K-Akt and NOP2,EPRS,FTSJ3,KIAA0020 may provide new direction for research of mechanisms of abnormal activation of fibrotic diseases including silicosis in incidence and development of multiple lung fibrotic diseases.